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智能反射面增强的全双工环境反向散射通信系统波束成形算法

张晓茜 徐勇军 吴翠先 黄崇文

张晓茜, 徐勇军, 吴翠先, 黄崇文. 智能反射面增强的全双工环境反向散射通信系统波束成形算法[J]. 电子与信息学报, 2024, 46(3): 914-924. doi: 10.11999/JEIT230356
引用本文: 张晓茜, 徐勇军, 吴翠先, 黄崇文. 智能反射面增强的全双工环境反向散射通信系统波束成形算法[J]. 电子与信息学报, 2024, 46(3): 914-924. doi: 10.11999/JEIT230356
ZHANG Xiaoxi, XU Yongjun, WU Cuixian, HUANG Chongwen. Beamforming Design for Reconfigurable Intelligent Surface Enhanced Full-duplex Ambient Backscatter Communication Networks[J]. Journal of Electronics & Information Technology, 2024, 46(3): 914-924. doi: 10.11999/JEIT230356
Citation: ZHANG Xiaoxi, XU Yongjun, WU Cuixian, HUANG Chongwen. Beamforming Design for Reconfigurable Intelligent Surface Enhanced Full-duplex Ambient Backscatter Communication Networks[J]. Journal of Electronics & Information Technology, 2024, 46(3): 914-924. doi: 10.11999/JEIT230356

智能反射面增强的全双工环境反向散射通信系统波束成形算法

doi: 10.11999/JEIT230356
基金项目: 国家自然科学基金(62271094),重庆市自然科学基金(CSTB2022NSCQ-LZX0009),重庆市教委科学技术研究项目(KJZD-K202200601),浙江省信息处理与通信网络重点实验室开放课题(IPCAN-2302)
详细信息
    作者简介:

    张晓茜:男,博士生,研究方向为反向散射通信、智能反射面等

    徐勇军:男,副教授,博士生导师,研究方向为反向散射通信、智能反射面、资源分配等

    吴翠先:女,正高级工程师,硕士生导师,研究方向为智能反射面、资源分配、共生无线电等

    黄崇文:男,教授,博士生导师,研究方向为智能反射面、机器学习、资源分配等

    通讯作者:

    徐勇军 xuyj@cqupt.edu.cn

  • 11 假设节点的分布情况为稀疏分布,该文探究的模型也同样可以迁移到用户密集分布的情况。2 根据文献[21],假设RIS 反射多次信号的功率可被忽略,同时考虑其在反射期间无能量损失。
  • 中图分类号: TN929.5

Beamforming Design for Reconfigurable Intelligent Surface Enhanced Full-duplex Ambient Backscatter Communication Networks

Funds: The National Natural Science Foundation of China (62271094), Natural Science Foundation of Chongqing (CSTB2022NSCQ-LZX0009), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), The Open Project of Zhejiang Provincial Key Laboratory of Information Processing and Communication Networking (IPCAN-2302)
  • 摘要: 当前传统环境反向散射通信存在双重衰落、障碍物阻挡和网络容量有限的问题。智能反射面(RIS)作为6G的关键候选技术因能够主动改善信号传输质量、提升通信系统传输性能而备受关注。为此,该文将RIS与全双工技术引入到环境反向散射通信系统,研究了考虑硬件损伤与RIS离散相移的RIS增强全双工环境反向散射通信系统波束成形算法。首先,考虑反射节点最小能量收集与服务质量约束、功率站最大发射功率约束和RIS相移约束,建立了总发射功率最小的波束成形优化问题。然后,利用交替优化、半正定松弛、变量替换、半正定规划将原非凸问题转化成可求解的凸优化问题。最后,仿真结果表明,所提算法比传统波束成形方法平均功耗降低了7.8%。
  • 图  1  RIS增强的全双工环境反向散射通信系统模型

    图  2  不同算法下HAP发射功率与反射节点到HAP距离$D$的关系

    图  3  不同算法下HAP发射功率与硬件损伤因子$\kappa $的关系

    图  4  不同算法下HAP发射功率与最小信干噪比$\gamma _k^{\min }$的关系

    图  5  不同算法下HAP发射功率与反射单元数$M$的关系

    图  6  不同算法下HAP发射功率与最小信干噪比$\gamma _k^{\min }$的关系

    图  7  不同算法下HAP发射功率与反射节点数量$K$的关系

    算法1 基于迭代的发射功率最小化波束成形算法
     初始化系统参数:$M$, $K$, $ {N}_{\mathrm{t}} $, ${N_{ {\rm{r} } } }$, $T$, ${{\boldsymbol{F}}_{\rm d} }$, ${{\boldsymbol{F}}_{\rm u} }$, ${{\boldsymbol{g}}_{ { {\rm{d} } } ,k} }$, ${{\boldsymbol{g}}_{ { {\rm{u} } } ,k} }$, ${{\boldsymbol{h}}_{ { {\rm{d} } } ,k} }$,
     ${{\boldsymbol{h}}_{ { {\rm{u} } } ,k} }$, ${ { {{\boldsymbol{\varTheta}} } }_{\rm u} }$, ${ { {{\boldsymbol{\varPhi}} } }_{\rm u} }$, ${ { {{\boldsymbol{\varTheta}} } }_{\rm d} }$, ${ { {{\boldsymbol{\varPhi}} } }_{\rm d} }$, $ {\kappa _{\rm d} } $, ${\kappa _{ {{\rm{u}}} ,k} }$, $b$, ${\mu _k}$, $ {\beta _k} $, $\bar \sigma _{ {{\rm{B}}} ,k}^2$, $\sigma _k^2$, $ \gamma _k^{\min } $,
     $ {P^{\max }} $, $ E_k^{\min } $, ${\boldsymbol{v}}_{\rm u} ^{(l)}$, ${\boldsymbol{v}}_{\rm d} ^{(l)}$, $ {\boldsymbol{w}}_k^{(l)} $;设置收敛精度$\varepsilon = {10^{ - 5} }$,最大
     迭代次数$ {L_{\max }} $,初始化$l = 1$,令$\mathcal{Q} = \displaystyle\sum\nolimits_{k = 1}^K { {\rm{Tr} }({ {\boldsymbol{w} }_k}{\boldsymbol{w} }_k^{\rm{H} })}$;
     (1) While $\left| { {\mathcal{Q}^{(l+1)} } - {\mathcal{Q}^{(l )} } } \right| \ge \varepsilon$或$l \le {L_{\max } }$ do
     (2)  Repeat
     (3)   固定$\{ {\boldsymbol{v}}_{\rm u} ^{(l)},{\boldsymbol{v}}_{\rm d} ^{(l)}\} $,求解式(11)得到$ {\boldsymbol{W}}_k^* $,进而得到$ {\boldsymbol{w}}_k^* $
         并更新$ {\boldsymbol{w}}_k^{(l + 1)} $;
     (4)  Until 收敛;
     (5)  Repeat
     (6)   固定$ \{ {\boldsymbol{w}}_k^{(l + 1)},{\boldsymbol{v}}_{\rm d} ^{(l)}\} $,求解式(17)得到${\boldsymbol{U}}_{\rm u} ^*$,进而得到${\boldsymbol{v}}_{\rm u} ^*$
         并更新${\boldsymbol{v}}_{\rm u} ^{(l + 1)}$;
     (7)  Until 收敛;
     (8)  Repeat
     (9)   固定$ {\boldsymbol{w}}_k^{(l + 1)} $和${\boldsymbol{v}}_{\rm u} ^{(l + 1)}$,求解式(22)得到${\boldsymbol{U}}_{\rm d} ^*$,进而得到
         ${\boldsymbol{v}}_{\rm d} ^*$并更新${\boldsymbol{v}}_{\rm d} ^{(l + 1)}$;
     (10) Until 收敛;
     (11) 并令$l = l + 1$将得到的$ \{ {\boldsymbol{w}}_k^{(l+1)},{\boldsymbol{v}}_{\rm u} ^{(l+1)},{\boldsymbol{v}}_{\rm d} ^{(l+1)}\} $代入计算
        $ {\mathcal{Q}^{(l+1)}} $;
     (12) End while
     (13) 得到$\{ {\boldsymbol{w} }_k^* = {\boldsymbol{w} }_k^{(l)},k \in \mathcal{K},{\boldsymbol{v}}_{\rm u} ^* = {\boldsymbol{v} }_{\rm u} ^{(l)},{\boldsymbol{v}}_{\rm d} ^* = {\boldsymbol{v} }_{\rm d} ^{(l)}\}$。
    下载: 导出CSV

    表  1  不同算法对比

    算法名称优化目标模式RIS相移硬件状态
    半双工算法[27]最小化
    发射功
    半双工/理想
    全双工算法[28]全双工/非理想
    RIS辅助算法[29]半双工连续相移理想
    所提算法全双工离散相移非理想
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-04
  • 修回日期:  2023-07-12
  • 网络出版日期:  2023-07-17
  • 刊出日期:  2024-03-27

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